19 дек. 2019 г. · In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data ...

Across a variety of non-iid graph datasets with tabular node features, our method achieves comparable or superior ... tabular modeling to node prediction tasks in ...

Researchers have used nearest neighbor graphs to transform classical machine learning problems on tabular data into node classification tasks to solve with.

26 сент. 2024 г. · Strengths: The authors propose multiple datasets which combine graph structure with tabular data -- specifically with heterogenous node data.

We propose an approach, called IGNNet (Interpretable Graph Neural. Network for tabular data), which constrains the learning algorithm to produce an.

First, we create a benchmark of diverse graphs with heterogeneous tabular node features and realistic prediction tasks. ... Machine learning for tabular data The ...

Using a hypergraph neural network on tabular data allows us to obtain a representation for each row from its corresponding hyperedge, as well as a ...

21 сент. 2023 г. · Researchers have used nearest neighbor graphs to transform classical machine learning problems on tabular data into node classification tasks to

The SDTR is a neural network which imitates a binary decision tree. Therefore, all neurons, like nodes in a tree, get the same input from the data instead of ...

In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges ...